In remote sensing imagery, it can often be difficult to consistently find corresponding pairs of imagery in the required domains at the same location due to cloud cover [1]}. To address this, we utilize an unpaired image-to-image translation framework known as cycleGAN [2]}. This leverages a pair of GANs to learn two transformations: one from domain X to Y, and one back from domain Y to X. These two GANs are bounded by the cycle-consistency loss: returning to our analogy of language translation, cycle-consistency ensures that a sentence translated from English to French, and then back to English is the same as the original sentence. In the case of cycleGAN, the two translators \(G:X \rightarrow Y\)  and \(F:Y\rightarrow X\)  should be inverses of one another and the translations are bijections, ensuring individual inputs and outputs can be connected in pairs in a valid manner [2]}.
Thus, given a pair of translators \(G\)  and \(F\) , the full objective is given by:
\(L(G,F,D_{X}, D_{Y}) = L_{GAN}(G, D_{Y}, X, Y) + L_{GAN}(F, D_{X}, X, Y) + \lambda L_{cycle}(G, F)\) 
